import torch

"""
    ['AbsTransform', 'AffineTransform', 'Bernoulli', 'Beta', 'Binomial', 
    'CatTransform', 'Categorical', 'Cauchy', 'Chi2', 'ComposeTransform', 
    'ContinuousBernoulli', 'CorrCholeskyTransform', 'CumulativeDistributionTransform', 
    'Dirichlet', 'Distribution', 'ExpTransform', 'Exponential', 'ExponentialFamily', 
    'FisherSnedecor', 'Gamma', 'Geometric', 'Gumbel', 'HalfCauchy', 'HalfNormal', 
    'Independent', 'IndependentTransform', 'InverseGamma', 'Kumaraswamy', 'LKJCholesky', 
    'Laplace', 'LogNormal', 'LogisticNormal', 'LowRankMultivariateNormal', 
    'LowerCholeskyTransform', 'MixtureSameFamily', 'Multinomial', 'MultivariateNormal', 
    'NegativeBinomial', 'Normal', 'OneHotCategorical', 'OneHotCategoricalStraightThrough', 
    'Pareto', 'Poisson', 'PositiveDefiniteTransform', 'PowerTransform', 'RelaxedBernoulli', 
    'RelaxedOneHotCategorical', 'ReshapeTransform', 'SigmoidTransform', 'SoftmaxTransform', 
    'SoftplusTransform', 'StackTransform', 'StickBreakingTransform', 'StudentT', 'TanhTransform', 
    'Transform', 'TransformedDistribution', 'Uniform', 'VonMises', 'Weibull', 'Wishart', 
    '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', 
    '__name__', '__package__', '__path__', '__spec__', 'bernoulli', 'beta', 'biject_to', 
    'binomial', 'categorical', 'cauchy', 'chi2', 'constraint_registry', 'constraints', 
    'continuous_bernoulli', 'dirichlet', 'distribution', 'exp_family', 'exponential', 
    'fishersnedecor', 'gamma', 'geometric', 'gumbel', 'half_cauchy', 'half_normal', 
    'identity_transform', 'independent', 'inverse_gamma', 'kl', 'kl_divergence', 
    'kumaraswamy', 'laplace', 'lkj_cholesky', 'log_normal', 'logistic_normal', 
    'lowrank_multivariate_normal', 'mixture_same_family', 'multinomial', 'multivariate_normal', 
    'negative_binomial', 'normal', 'one_hot_categorical', 'pareto', 'poisson', 'register_kl', 
    'relaxed_bernoulli', 'relaxed_categorical', 'studentT', 'transform_to', 
    'transformed_distribution', 'transforms', 'uniform', 'utils', 'von_mises', 
    'weibull', 'wishart']
"""
print(dir(torch.distributions))
